Airfield pavement detection method based on seismic background noise imaging technology

Abstract

The invention provides an airfield pavement detection method based on an earthquake background noise imaging technology, and relates to the technical field of airfield pavement detection. Comprising the following steps: acquiring seismic background noise data of an airport pavement to be measured; preprocessing the seismic background noise data to obtain preprocessed noise data; carrying out cross-correlation calculation on the preprocessed noise data and carrying out empirical Green function extraction; according to the empirical Green function, a surface wave group velocity frequency dispersion curve is extracted by adopting spatial autocorrelation and multiple filtering methods; inverting shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model; determining a shear wave velocity distribution diagram according to the three-dimensional velocity model; and determining a detection result of the airfield pavement to be detected according to the transverse wave velocity distribution diagram. According to the invention, the problems of low detection precision and poor real-time performance of a common detection technology in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of airport pavement inspection technology, and in particular to an airport pavement inspection method based on seismic background noise imaging technology. Background Technology

[0002] A certain airport runway and apron area exhibits pavement slab delamination at the bottom, which is progressively worsening. After the concrete pavement slabs delaminate, they are in a state of weak support or cantilever stress, making them prone to structural defects such as slab breakage or corner fractures under heavy traffic and loads, requiring close monitoring. Pavement delamination is common in rainy southern regions of the country. Grouting at the bottom is currently the most effective treatment for delamination, but it is costly. In most cases, maintenance should be carried out at an appropriate time based on the actual condition of the pavement. It is generally recommended to conduct targeted HWD (Hill Weight Deflectometer) monitoring of the pavement based on existing test results to promptly grasp the development of pavement delamination.

[0003] Currently, commonly used testing technologies include: non-destructive testing (NDT): ground penetrating radar, deflectometers, etc.; and destructive testing: core drilling. The advantages of these methods are: they are mature and widely used. The disadvantages are: testing accuracy depends on the number of sampling points or the sampling density; they can affect normal airport operations; the accuracy of indirect testing methods is questionable; and they lack real-time performance, making it difficult to track changing trends and achieve continuous monitoring. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an airport pavement inspection method based on seismic background noise imaging technology. This invention solves the problems of low detection accuracy and poor real-time performance of commonly used detection technologies in the prior art.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] An airport pavement inspection method based on seismic background noise imaging technology includes:

[0007] Collect seismic background noise data of the airport pavement to be tested;

[0008] The earthquake background noise data is preprocessed to obtain preprocessed noise data;

[0009] The preprocessed noise data is cross-correlation calculated to extract the empirical Green's function;

[0010] Based on the empirical Green's function, spatial autocorrelation and multiple filtering methods are proposed to extract the surface wave group velocity dispersion curve;

[0011] Based on the surface wave group velocity dispersion curve, the shear wave velocity distribution of each layer of the track surface is inverted, and a three-dimensional velocity model is constructed.

[0012] The transverse wave velocity distribution map is determined based on the three-dimensional velocity model.

[0013] The detection results of the airport pavement under test are determined based on the shear wave velocity distribution map.

[0014] Preferably, the acquisition of seismic background noise data of the airport pavement to be tested includes:

[0015] Determine the pavement dimensions and detection accuracy target of the airport pavement to be tested;

[0016] The spacing between seismic detectors is determined based on the pavement dimensions and the target detection accuracy.

[0017] Seismic detectors are deployed in a regular grid pattern on the runway surface of the airport to be tested, according to the specified spacing, to collect seismic background noise data.

[0018] Preferably, the method for preprocessing the seismic background noise data includes:

[0019] Filtering, noise reduction, and normalization processing.

[0020] Preferably, the step of performing cross-correlation calculation on the preprocessed noise data and extracting the empirical Green's function includes:

[0021] Calculate the cross-correlation function of each preprocessed noise data according to the preset time range;

[0022] The empirical Green's function is obtained by superimposing the cross-correlation functions.

[0023] Preferably, the step of inverting the shear wave velocity distribution of each layer of the track surface based on the surface wave group velocity dispersion curve and constructing a three-dimensional velocity model includes:

[0024] The region is divided according to the surface wave group velocity dispersion curve to obtain a two-dimensional mesh;

[0025] The vertical distribution of seismic shear wave velocity below each grid is obtained by performing pure path dispersion inversion on the two-dimensional grid.

[0026] Based on the vertical distribution of the seismic shear wave velocity, the dynamic variation characteristics of the three-dimensional fine wave velocity structure of the seismic shear wave are determined to construct a three-dimensional velocity model.

[0027] Preferably, the preset time range is 5-10 minutes.

[0028] Preferably, the test results include: structural integrity, cracks, and voids.

[0029] The present invention discloses the following technical effects:

[0030] This invention provides an airport pavement inspection method based on seismic background noise imaging technology, comprising: acquiring seismic background noise data of the airport pavement to be tested; preprocessing the seismic background noise data to obtain preprocessed noise data; performing cross-correlation calculation on the preprocessed noise data to extract an empirical Green's function; extracting surface wave group velocity dispersion curves using spatial autocorrelation and multiple filtering methods based on the empirical Green's function; inverting the shear wave velocity distribution of each layer of the pavement based on the surface wave group velocity dispersion curves to construct a three-dimensional velocity model; determining the shear wave velocity distribution map based on the three-dimensional velocity model; and determining the inspection result of the airport pavement to be tested based on the shear wave velocity distribution map. This invention improves inspection accuracy by inverting shear wave velocity based on the surface wave dispersion curves and performing gridded tomography based on the three-dimensional velocity model; and improves the real-time performance of data detection by utilizing pre-deployed detectors. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of an airport pavement inspection method based on seismic background noise imaging technology is provided for an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of sensor arrangement and imaging coverage provided in an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram of the three-dimensional structure imaging of the medium beneath an airport runway, provided as an embodiment of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] like Figure 1 As shown, this invention provides an airport pavement inspection method based on seismic background noise imaging technology, comprising:

[0038] Step 100: Collect seismic background noise data of the airport pavement to be tested;

[0039] Step 200: Preprocess the earthquake background noise data to obtain preprocessed noise data;

[0040] Step 300: Perform cross-correlation calculation on the preprocessed noise data and extract the empirical Green's function;

[0041] Step 400: Based on the empirical Green's function, spatial autocorrelation and multiple filtering methods are proposed to extract the surface wave group velocity dispersion curve;

[0042] Step 500: Invert the shear wave velocity distribution of each layer of the track surface based on the surface wave group velocity dispersion curve, and construct a three-dimensional velocity model;

[0043] Step 600: Determine the shear wave velocity distribution map based on the three-dimensional velocity model;

[0044] Step 700: Determine the detection result of the airport pavement to be tested based on the shear wave velocity distribution map.

[0045] Furthermore, the acquisition of seismic background noise data of the airport pavement to be tested includes:

[0046] Determine the pavement dimensions and detection accuracy target of the airport pavement to be tested;

[0047] The spacing between seismic detectors is determined based on the pavement dimensions and the target detection accuracy.

[0048] Seismic detectors are deployed in a regular grid pattern on the runway surface of the airport to be tested, according to the specified spacing, to collect seismic background noise data.

[0049] Specifically, sensor deployment: High-sensitivity seismic detectors are deployed in a regular grid pattern in the area of ​​the airport pavement to be measured, forming a dense observation network. The sensor spacing is determined based on the pavement dimensions and detection accuracy requirements.

[0050] like Figure 2 As shown, surface waves between any two stations were extracted from the soil surface area 1m away from both sides of an airport runway at a spacing of 20m, and the data information was fully explored: N×(N-1) / 2 dispersion lines were extracted from 80 stations, resulting in 3160 dispersion curves, achieving a spatial resolution of 10m and a detection depth of 70m.

[0051] After calculating the empirical Green's function between stations, different waveform signals can be extracted, such as longitudinal waves, Rayleigh waves of different orders, and Love waves, with surface waves accounting for the majority of the energy. Due to the inhomogeneity of the Earth's internal medium, surface waves exhibit dispersion during propagation, resulting in different propagation velocities and sensitive depth ranges for surface waves of different periods. Based on this characteristic of surface waves, the obtained empirical Green's function can be used to extract the group velocity and phase velocity dispersion curves of Rayleigh and Love waves between station pairs. These dispersion curves can then be used for surface wave imaging and shear wave velocity inversion.

[0052] Earthquake background noise data acquisition: Continuously record environmental background noise signals (duration ≥ 24 hours), and synchronously store timestamps and spatial coordinate information. The recording time for acquiring earthquake background noise data is set according to the actual situation.

[0053] Furthermore, the method for preprocessing the earthquake background noise data includes:

[0054] Filtering, noise reduction, and normalization processing.

[0055] Specifically, the original signal is filtered (0.1-50Hz), denoised (wavelet thresholding method), and normalized to extract the effective frequency band.

[0056] Using continuous waveform data recorded by sensors deployed around the runway, the raw data preprocessing includes: removing instrument response, removing mean, removing tilt, and cutting into unit length data (such as minutes, hours, etc.); using data preprocessing methods such as moving absolute value averaging to suppress interference from seismic signals, and performing spectral whitening on the processed waveforms in the required frequency band.

[0057] Furthermore, the step of performing cross-correlation calculation on the preprocessed noise data and extracting the empirical Green's function includes:

[0058] Calculate the cross-correlation function of each preprocessed noise data according to the preset time range;

[0059] The empirical Green's function is obtained by superimposing the cross-correlation functions.

[0060] Specifically, cross-correlation analysis is performed on the noise signals between any two sensors to extract the empirical Green's function and reconstruct the surface wave propagation characteristics of the pavement structure.

[0061] An empirical Green's function between stations is obtained using a frequency domain waveform cross-correlation method: the cross-correlation function between two stations is calculated every 5-10 minutes, and the long-term cross-correlation functions are then superimposed as required to obtain the empirical Green's function. To improve the signal-to-noise ratio of the empirical Green's function, the positive and negative half-axis of the cross-correlation waveforms are symmetrically superimposed to enhance the signal-to-noise ratio of the surface wave signal. For extracting the surface wave dispersion curve, spatial autocorrelation and multiple filtering methods are proposed to extract the surface wave group velocity dispersion curve.

[0062] Furthermore, the method of inverting the shear wave velocity distribution of each layer of the track surface based on the surface wave group velocity dispersion curve and constructing a three-dimensional velocity model includes:

[0063] The region is divided according to the surface wave group velocity dispersion curve to obtain a two-dimensional mesh;

[0064] The vertical distribution of seismic shear wave velocity below each grid is obtained by performing pure path dispersion inversion on the two-dimensional grid.

[0065] Based on the vertical distribution of the seismic shear wave velocity, the dynamic variation characteristics of the three-dimensional fine wave velocity structure of the seismic shear wave are determined to construct a three-dimensional velocity model.

[0066] Specifically, based on the surface wave group velocity dispersion curve, the shear wave velocity distribution of each layer of the track is inverted, a three-dimensional velocity model is constructed, and velocity anomaly zones are identified.

[0067] The surface wave tomography method includes the following steps: First, the inversion area is divided into grids, and the two-dimensional distribution of phase velocity or group velocity of each period is obtained by inversion, thus obtaining the pure path dispersion curve of each grid node; then, the pure path dispersion of each grid is inverted to obtain the vertical distribution of seismic shear wave velocity below each grid, and finally the dynamic change characteristics of the three-dimensional fine wave velocity structure of seismic shear waves in the medium below the runway at different times are obtained.

[0068] Furthermore, pavement inspection involves analyzing the structural integrity of the airport pavement based on the shear wave velocity distribution map to identify potential defects such as cracks and voids. For example... Figure 3 The low-speed zone corresponds to voids and cracks, while the high-speed zone corresponds to a dense structure.

[0069] Regarding the time interval for dynamic monitoring and imaging, based on the actual data from the airport runway seismic array, attempts can be made to dynamically obtain the two-dimensional and three-dimensional wave velocity structure change characteristics of the medium beneath the airport runway at time intervals of days, weeks, months, quarters, and years. Based on the aforementioned dynamic monitoring results, direct evidence can be provided for the analysis of subsidence, geological defects, and groundwater level changes in the medium beneath the runway. When abnormal defects are detected in localized areas of the airport runway through the aforementioned dynamic monitoring, active source seismic imaging can be performed on key localized areas of the airport runway to obtain higher-resolution subsurface medium structural characteristics, thereby providing a basis for defect remediation.

[0070] Determining the shear wave velocity distribution map based on the three-dimensional velocity model includes:

[0071] The fast-traveling surface wave imaging method is adopted, which uses an alternating forward and inverse modeling approach to perform surface wave imaging. This method continuously updates the inversion model and adds perturbations to the objective function for secondary approximation, so that the forward modeling prediction travel time of the final inverted velocity model forms a good fit with the observed travel time data.

[0072] The perturbation of the model after a certain iteration can be calculated to obtain the model after the next iteration, and finally a three-dimensional seismic shear wave velocity structure model at different depths in the lower part of the target area can be obtained.

[0073] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0074] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An airport pavement inspection method based on seismic background noise imaging technology, characterized in that, include: Collect seismic background noise data of the airport pavement to be tested; The earthquake background noise data is preprocessed to obtain preprocessed noise data; The preprocessed noise data is cross-correlation calculated to extract the empirical Green's function; Based on the empirical Green's function, spatial autocorrelation and multiple filtering methods are proposed to extract the surface wave group velocity dispersion curve; Based on the surface wave group velocity dispersion curve, the shear wave velocity distribution of each layer of the track surface is inverted, and a three-dimensional velocity model is constructed. The transverse wave velocity distribution map is determined based on the three-dimensional velocity model. The detection results of the airport pavement under test are determined based on the shear wave velocity distribution map.

2. The airport pavement inspection method based on seismic background noise imaging technology according to claim 1, characterized in that, The collection of seismic background noise data for the airport pavement to be tested includes: Determine the pavement dimensions and detection accuracy target of the airport pavement to be tested; The spacing between seismic detectors is determined based on the pavement dimensions and the target detection accuracy. Seismic detectors are deployed in a regular grid pattern on the runway surface of the airport to be tested, according to the specified spacing, to collect seismic background noise data.

3. The airport pavement inspection method based on seismic background noise imaging technology according to claim 1, characterized in that, A method for preprocessing the earthquake background noise data includes: Filtering, noise reduction, and normalization processing.

4. The airport pavement inspection method based on seismic background noise imaging technology according to claim 1, characterized in that, The step of performing cross-correlation calculation on the preprocessed noise data and extracting the empirical Green's function includes: Calculate the cross-correlation function of each preprocessed noise data according to the preset time range; The empirical Green's function is obtained by superimposing the cross-correlation functions.

5. The airport pavement inspection method based on seismic background noise imaging technology according to claim 1, characterized in that, The method involves inverting the shear wave velocity distribution of each layer of the track surface based on the surface wave group velocity dispersion curve, and constructing a three-dimensional velocity model, including: The region is divided according to the surface wave group velocity dispersion curve to obtain a two-dimensional mesh; The vertical distribution of seismic shear wave velocity below each grid is obtained by performing pure path dispersion inversion on the two-dimensional grid. Based on the vertical distribution of the seismic shear wave velocity, the dynamic variation characteristics of the three-dimensional fine wave velocity structure of the seismic shear wave are determined to construct a three-dimensional velocity model.

6. The airport pavement inspection method based on seismic background noise imaging technology according to claim 4, characterized in that, The preset time range is 5-10 minutes.

7. The airport pavement inspection method based on seismic background noise imaging technology according to claim 1, characterized in that, The test results include: structural integrity, cracks, and voids.

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